Process control systems are widely used in factories and/or plants in which products are manufactured or processes are controlled (e.g., chemical manufacturing, power plant control, etc.). Process control systems are also used in the harvesting of natural resources such as, for example, oil and gas drilling and handling processes, etc. In fact, virtually any manufacturing process, resource harvesting process, etc. can be automated through the application of one or more process control systems. It is believed the process control systems will eventually be used more extensively in agriculture as well.
Process control systems, like those used in chemical, petroleum or other processes, typically include one or more centralized or decentralized process controllers communicatively coupled to at least one host or operator workstation and to one or more process control and instrumentation devices, such as field devices, via analog, digital or combined analog/digital buses. Field devices, which may be, for example valves, valve positioners, switches, transmitters, and sensors (e.g., temperature, pressure and flow rate sensors), perform functions within the process such as opening or closing valves and measuring process parameters. The process controller receives signals indicative of process measurements or process variables made by or associated with the field devices and/or other information pertaining to the field devices, uses this information to implement a control routine and then generates control signals which are sent over one or more of the buses to the field devices to control the operation of the process. Information from the field devices and the controller is typically made available to one or more applications executed by an operator workstation to enable an operator to perform desired functions with respect to the process, such as viewing the current state of the process, modifying the operation of the process, etc.
The various devices within the process plant may be interconnected in physical and/or logical groups to create a logical process, such as a control loop Likewise, a control loop may be interconnected with other control loops and/or devices to create sub-units. A sub-unit may be interconnected with other sub-units to create a unit, which in turn, may be interconnected with other units to create an area. Process plants generally include interconnected areas, and business entities generally include process plants which may be interconnected. As a result, a process plant includes numerous levels of hierarchy having interconnected assets, and a business enterprise may include interconnected process plants. In other words, assets related to a process plant, or process plants themselves, may be grouped together to form assets at higher levels.
Adaptive process control loops have been developed that adaptively designed self-tuning controllers for process control systems. Generally, adaptive process control loops are based on a model parameter interpolation. According to the model parameter interpolation, a candidate process model may be defined by a predetermined set of models. Each of the models may be characterized by a plurality of parameters and for each model, each of the parameters has a respective value that is selected from a set of predetermined initialization values corresponding to the parameter. The valuation of each of the models may include computation of a model squared error and computation of a Norm that is derived from the models square errors calculated for the models. The Norm value is assigned to every parameter value represented in the model that is represented in an evaluation scan. As repeated evaluations of models are conducted, an accumulated Norm is calculated for each parameter value. The accumulated Norm is the sum of all Norms that have been assigned to the parameter value in the course of model evaluations. Subsequently, an adaptive parameter value is calculated for each parameter of the process control loop. The adaptive parameter value may be a weighted average of the initialization values assigned to the respective parameters. The set of adaptive process parameter values are then used a redesigned the adaptive process control loop, and in particular the adaptive process control loop controller.
Process control loop performance measurements and diagnostics of possible causes of degradation in a process control loop is a common challenge for process control personnel. Generally, process control loop diagnostics involves a monitoring system that measures and presents information to an operator, such as variability of each variable in a process control loop, control block modes, process control loop input and output status, etc. The process control loop diagnostics help identify process control loops that are performing inadequately, process control loop having a bad mode and process control loops having a bad status. Accordingly, problematic process control loops can be identified, and the problems with such process control loops can be identified. However, identification of the causes of such problems generally requires separate diagnostic procedures.
In one example of a process control loop monitoring and diagnostic procedure, causes of process control loop oscillations are identified after detecting process control loop oscillations. Potential causes of process control loop oscillation are identified or categorized as external disturbances, such as a process control loop device problem (e.g., valve problem) or as a tuning problem. However, process control loop diagnostics often require significant manipulation of the process control loop. For example, a process control loop may require manipulation by switching the process control loop to manual or by changing the tuning of a process controller for the process control loop. Still further, process control loop diagnostics are often intended for execution under the supervision of a process control operator or other personnel.
Undesirable behavior of process control valves is often a significant contributor to poor process control loop performance and destabilization of a process operation. Valve diagnostics is often performed by process control loop special testing in a manual mode, and the test results are used for calculating valve resolution and dead band. More comprehensive diagnostic techniques involve measuring several valve parameters including backlash, dead time and response time. However, these diagnostic techniques require application of special test sequences and measurement of more valve operational parameters than are available in regular process control loops, including positioner pressure.
Several valve diagnostic techniques have been developed to detect valve resolution, dead band and hysteresis. One example of a technique for automatic valve diagnostics is based on developing a cross-correlation function of a process variable and a process control loop controller output for a self-regulating process. A negative phase shift of the function indicates no stiction, while a positive phase shift indicates valve stiction.
In another example, an integrating process stiction is identified by applying a histogram shape of the second derivative of a process output value. A single histogram peak in the center indicates stiction, while two-sided peaks with a valley in the middle indicates no valve stiction. However, the results are qualitative and highly dependent on process control loop controller tuning.
Additional techniques estimate a percentage of time when valve position does not change while a process control loop controller output changes, utilize a plot shape of a process variable and controller output and/or utilize a curve fitting method for detecting valve stiction in oscillatory control loops. Stiction is indicated if the controller output curve for a self-regulating process or the process variable curve for an integrating process more closely corresponds to a triangular shape than a sinusoid shape. The curve shape is assessed by a stiction value. Stiction is considered absent in valve when the value is greater than or equal to zero and less than or equal to 0.4, considered undefined if the value is greater than 0.4 and less than 0.6, and considered present in the valve when the value is greater than or equal to 0.6 and less than or equal to 1. A further technique assumes a linear process model and a nonlinear valve model, and uses test data to identify both models. The identified valve model provides hysteresis of the valve, but without distinguishing between dead band and resolution.
Despite the advent of the above techniques, there is still a need for less complex techniques and for techniques which diagnose dead band and resolution. These demands motivated the authors setting the primary objective of adding to the existing approaches a simple and practical diagnostic concept. Further, process control loop diagnostics is particularly important for adaptive process control loops. For example, adaptation of an adaptive process control loop may cause instability, which is not caused by improper adaptation, but rather by a process control loop device problem (e.g., a sticky valve) or a measurement failure. However, manual or semi-manual diagnostic procedures are not adequate for the adaptive process control loop, which often operate without supervision.